Molecular Design Computational Intelligence Unit

Research summary

The aim of Molecular Design Computational Intelligence Unit is to develop novel hybrid computational methods of molecular simulation and artificial intelligence for rational drug design. Combining artificial intelligence with molecular simulation will extend the applicability of in-silico drug design in terms of both accuracy and efficiency. In addition to the rational design of conventional small molecules, middle-sized and macro molecules are also the targets of newly developed computational methods.

Main research results

In our research unit, we focus on two promising new approaches in drug discovery: cyclic peptides and nucleic acid therapeutics.

Cyclic Peptide Research:
Cyclic peptides are attracting attention as potential drugs that can block interactions between proteins inside cells—something that traditional small-molecule drugs often cannot do. However, one of the major challenges is that it is very difficult to predict whether these molecules can pass through cell membranes, which is essential for them to work inside the body.

To tackle this issue, our unit has developed special computational models (called force field parameters) to simulate the behavior of non-standard amino acids that are commonly found in cyclic peptides. Using an advanced simulation technique called gREST/REUS, which helps us observe rare events like membrane permeation more effectively, we study how these peptides cross the membrane.

Recent simulation results have revealed that for some cyclic peptides, their shape at the membrane-water interface—the boundary between the cell membrane and surrounding water—greatly affects how easily they can pass through the membrane. These findings are expected to contribute significantly to developing better prediction methods for the membrane permeability of cyclic peptides.

Nucleic Acid Therapeutics Research:
Nucleic acid drugs are a new type of medicine that work by targeting gene expression at an earlier stage than conventional drugs. This means they can potentially treat diseases that were previously difficult to target using small molecules or antibodies.

Our unit is working on designing antisense oligonucleotides, a type of nucleic acid drug that can either control RNA splicing (a process that affects how genes are read) or lead to the degradation of disease-related RNA. We have developed both a database and a web-based tool to support the design of these molecules, especially those that regulate RNA splicing. These resources are publicly available at https://eskip-finder.org (Chiba et al., 2021; Zhu et al., 2023). Using this platform, we can design effective antisense drugs based on their sequence and structure.

Representative papers

Shigeyuki Matsumoto, Hiroaki Iwata, Yuta Isaka, Ryo Kanada, Shoichi Ishida, Biao Ma, Yoshinobu Akinaga, Mitsugu Arakia, Shuntaro Chiba, Kei Terayama, Ryosuke Kojima, Atsushi Tokuhisab, Yohei Haradaa,Kazuhiro Takemurae, Teruki Honmac, Akio Kitao eand Yasushi Okuno:
"Precision spatiotemporal analysis of large-scale compound–protein interactions through molecular dynamics simulation"
PNAS Nexus, 4: pgaf094. (2025).
Kaho Shionoya, Jae-Hyun Park, Toru Ekimoto, Junko S. Takeuchi, Junki Mifune, Takeshi Morita, Naito Ishimoto, Haruka Umezawa, Kenichiro Yamamoto, Chisa Kobayashi, Atsuto Kusunoki, Norimichi Nomura, So Iwata, Masamichi Muramatsu, Jeremy R. H. Tame, Mitsunori Ikeguchi, Sam-Yong Park, Koichi Watashi:
"Structural basis for hepatitis B virus restriction by a viral receptor homologue"
Nature Communications, 15: 9241. (2024).
Mitsugu Araki, Toru Ekimoto, Kazuhiro Takemura, Shigeyuki Matsumoto, Yunoshin Tamura, Hironori Kokubo, Gert-Jan Bekker, Tsutomu Yamane, Yuta Isaka, Yukari Sagae, Narutoshi Kamiya, Mitsunori Ikeguchi, Yasushi Okuno:
"Molecular dynamics unveils multiple-site binding of inhibitors with reduced activity on the surface of dihydrofolate reductase"
Journal of the American Chemical Society, 146(42): 28685–28695. (2024).
Masao Inoue, Toru Ekimoto, Tsutomu Yamane, and Mitsunori Ikeguchi:
"Computational Analysis of Activation of Dimerized Epidermal Growth Factor Receptor Kinase Using the String Method and Markov State Model"
Journal of Chemical Information and Modeling, 64(9): 3884-3895. (2024).
Ryo Kanada, Atsushi Tokuhisa, Yusuke Nagasaka, Shingo Okuno, Koichiro Amemiya, Shuntaro Chiba, Gert-Jan Bekker, Narutoshi Kamiya, Koichiro Kato, Yasushi Okuno:
"Enhanced Coarse-Grained Molecular Dynamics Simulation with a Smoothed Hybrid Potential Using a Neural Network Model"
Journal of Chemiccal Theory and Computation,20(1): 7-17, (2024).
Naoki Ogawa, Masateru Ohta, Mitsunori Ikeguchi:
"Conformational Selectivity of ITK Inhibitors: Insights from Molecular Dynamics Simulations"
Journal of Chemical Information and Modeling,63(24): 7860-7872, (2023).
Alex Zhu,Shuntaro Chiba,Yuki Shimizu,Katsuhiko Kunitake,Yasushi Okuno,Yoshitsugu Aoki,Toshifumi Yokota:
"Ensemble-Learning and Feature Selection Techniques for Enhanced Antisense Oligonucleotide Efficacy Prediction in Exon Skipping"
Pharmaceutics, 15, 7, 1808, 2023.
Hideaki Ohtomo, Tsutomu Yamane, Takashi Oda, Noriyuki Kodera, Jun-ichi Kurita, Yasuo Tsunaka, Romain Amyot, Mitsunori Ikeguchi, Yoshifumi Nishimura:
"Dynamic Solution Structures of Whole Human NAP1 Dimer Bound to One and Two Histone H2A-H2B Heterodimers Obtained by Integrative Methods"
Journal of Molecular Biology, 435(15): 168189. (2023).
Tsutomu Yamane,Takahiro Nakayama,Toru Ekimoto,Masao Inoue,Keigo Ikezaki ,Hiroshi Sekiguchi,Masahiro Kuramochi,Yasuo Terao,Ken Judai,Minoru Saito ,Mitsunori Ikeguchi,Yuji C. Sasaki:
"Comparison of the Molecular Motility of Tubulin Dimeric Isoforms: Molecular Dynamics Simulations and Diffracted X-ray Tracking Study"
International Journal of Molecular Sciences, 24(20): 15423. (2023).

Unit Leader Mitsunori Ikeguchi

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